Biology subjects

Ferat, V.

Publications and source records attributed to Ferat, V..

4 recordsLinked to original sources

bioRxiv2026

The Dry Truth: Hair-Wetting Improves Dry Electrode EEG Signal Quality

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

neuroscience↗

bioRxiv2025

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

Current electroencephalogram (EEG) denoising methods struggle to remove the complex physiological and environmental artifacts typical of real-world settings, which both hinders the isolation of true neural activity and limits the technologys translational potential. We present the Generalized Eigenvalue De-Artifacting Instrument (GEDAI), a novel algorithm for denoising highly contaminated EEG. GEDAI employs leadfield filtering to selectively remove noise and artifacts that diverge from a theoretically defined EEG forward model. This approach offers unique advantages over existing solutions, including 1) denoising of highly corrupt recordings without "clean" reference data, 2) single-step correction of artifactual epochs and bad channels, 3) unsupervised detection of brain and noise components based on the signal and noise subspace alignment index (SENSAI). In ground-truth simulations with synthetic and empirical EEG contaminated with realistic artifacts (EOG, EMG, noise), GEDAI globally outperformed leading denoising techniques based on principal component analysis (ASR) and independent component analysis (IClabel, MARA), revealing large effect sizes in challenging scenarios with simultaneous artifact mixtures, low signal-to-noise ratio (-9 dB), and high temporal contamination (up to 100%). Its superior denoising also enhanced neurobehavioral predictions, yielding highest accuracies in ERP classification and brain fingerprinting. GEDAIs autonomy, computational speed and noise-resilience could find future applications in 1) real- world medical, mobile and dry electrode EEG recordings 2) magnetoenecephalography (MEG) denoising (given the shared M/EEG forward model), and 3) real-time brain-computer interfaces (BCIs). The Matlab code for GEDAI is available as an open-source EEGLAB plugin at https://github.com/neurotuning/GEDAI-master

neuroscience↗

bioRxiv2023

Self-regulation of attention in children in a virtual classroom environment: a feasibility study

Attention is a crucial cognitive function that enables us to selectively focus on relevant information from the surrounding world to achieve our goals. When this sustained ability to direct attention is impaired, individuals face significant challenges in everyday life. This is the case for children with Attention Deficit Hyperactivity Disorder (ADHD), a complex neurodevelopmental disorder characterized by impulsive and inattentive behavior. While psychostimulant medications are currently the most effective treatment for ADHD, they often come with unwanted side effects, and sustaining the benefits can be difficult for many children. Therefore, it is imperative to explore non-pharmacological treatments that offer longer-lasting outcomes. Here, we proposed a groundbreaking protocol that combines electroencephalography-based neurofeedback (EEG-NFB) with virtual reality (VR) as an innovative approach to treating attention deficits. By integrating a virtual classroom environment, we aimed to enhance the transferability of attentional control skills while simultaneously increasing motivation and interest among children. The present study demonstrated the feasibility of this approach through an initial assessment involving a small group of healthy children, showcasing its potential for future evaluation in children diagnosed with ADHD. Encouragingly, the preliminary findings indicated high engagement rates and positive feedback from the children participating in the study. Additionally, the pre-and post-protocol assessments using EEG and fMRI recordings appeared to converge towards an improvement in attentional function. Although further validation is required to establish the efficacy of the proposed protocol, it represents a significant advancement in the field of neurofeedback therapy for ADHD. The integration of EEG-NFB and VR presents a novel avenue for enhancing attentional control and addressing behavioral challenges in children with ADHD.

neuroscience↗

bioRxiv2020

Beyond broadband: towards a spectral decomposition of EEG microstates

Originally applied to alpha oscillations in the 1970s, MS analysis has since been used to decompose mainly broadband EEG signals (e.g. 1-40 Hz). We hypothesized that MS decomposition within separate, narrow frequency bands could provide more fine-grained information for capturing the spatio-temporal complexity of multichannel EEG. In this study using a large open-access dataset (n=203), we decomposed EEG recordings into 4 classical frequency bands (delta, theta, alpha, beta) in order to compare their individual MS segmentations using mutual information as well as traditional MS measures (e.g. mean duration, time coverage). Firstly, we confirmed that MS topographies were spatially equivalent across all frequencies, matching the canonical broadband maps (A, B, C, D). Interestingly however, we observed strong informational independence of MS temporal sequences between spectral bands, together with significant divergence in traditional MS measures. For example, relative to broadband, alpha/beta band dynamics displayed greater time coverage of maps A & B, while map D was more prevalent in delta/theta bands. Moreover, by using a frequency-specificMS taxonomy (e.g. {theta}A, C), we were able to predict the eyes-open vs closed-behavioural state significantly better using alpha-band MS features compared with broadband ones (80% vs 73% accuracy). Overall, our findings demonstrate the value and validity of spectrally-specific MS analyses, which may prove useful for identifying new neural mechanisms in fundamental research and/or for biomarker discovery in clinical populations.

neuroscience↗